ISPRS-ArchivesISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information SciencesISPRS-ArchivesInt. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.2194-9034Copernicus PublicationsGöttingen, Germany10.5194/isprs-archives-XLI-B2-277-2016ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN
MARKOV RANDOM FIELDAguilarF. J.1AguilarM. A.1BlancoJ. L.1NemmaouiA.1García LorcaA. M.21Dept. of Engineering, University of Almería, 04120 Almería, Spain2Dept. of Geography, University of Almería, 04120 Almería, Spain07062016XLI-B2277284This article is available from https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/277/2016/isprs-archives-XLI-B2-277-2016.htmlThe full text article is available as a PDF file from https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B2/277/2016/isprs-archives-XLI-B2-277-2016.pdf

Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use
classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field
(GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a
set of LiDAR data (0.87 points/m<sup>2</sup>) provided by the Spanish Government (PNOA Programme) over a complex working area mainly
covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the
original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were
used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the
set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known
Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the
LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding
accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the
GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the
DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.